Time varying Markov process with partially observed aggregate data: An application to coronavirus

被引：13
作者：
Gourieroux, C. [1 ,2 ,3 ]
Jasiak, J. [4 ]
机构：
[1] Univ Toronto, Toronto, ON, Canada
[2] Toulouse Sch Econ, Toulouse, France
[3] CREST, Toulouse, France
[4] York Univ, Toronto, ON, Canada
来源：
JOURNAL OF ECONOMETRICS | 2023年 / 232卷 / 01期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Markov process; Partial observability; Information recovery; Estimating equations; SIR model; Coronavirus; Infection rate;
D O I：
10.1016/j.jeconom.2020.09.007
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
A major difficulty in the analysis of Covid-19 transmission is that many infected individuals are asymptomatic. For this reason, the total counts of infected individuals and of recovered immunized individuals are unknown, especially during the early phase of the epidemic. In this paper, we consider a parametric time varying Markov process of Coronavirus transmission and show how to estimate the model parameters and approximate the unobserved counts from daily data on infected and detected individuals and the total daily death counts. This model-based approach is illustrated in an application to French data, performed on April 6, 2020.(c) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码：35 / 51
页数：17
相关论文
共 38 条
[1]   SOME DISCRETE-TIME SI, SIR, AND SIS EPIDEMIC MODELS [J].
ALLEN, LJS .
MATHEMATICAL BIOSCIENCES, 1994, 124 (01) :83-105
[2]   A Simple Planning Problem for COVID-19 Lock-down, Testing, and Tracing [J].
Alvarez, Fernando ;
Argente, David ;
Lippi, Francesco .
AMERICAN ECONOMIC REVIEW-INSIGHTS, 2021, 3 (03) :367-382
[3]  
Boatto S., 2018, HAL 01677886
[4]  
Brauer F, 2001, Mathematical models in population biology and epidemiology
[5]   TIME SERIES ANALYSIS VIA MECHANISTIC MODELS [J].
Breto, Carles ;
He, Daihai ;
Ionides, Edward L. ;
King, Aaron A. .
ANNALS OF APPLIED STATISTICS, 2009, 3 (01) :319-348
[6]  
Brown G., 2020, ESTIMATING UNDETECTE
[7]  
Chudik Alexander, 2020, Voluntary and Mandatory Social Distancing: Evidence on COVID-19 Exposure Rates from Chinese Provinces and Selected Countries, DOI DOI 10.3386/W27039
[8]  
CRESSIE N, 1984, J ROY STAT SOC B MET, V46, P440
[9]   Capturing the time-varying drivers of an epidemic using stochastic dynamical systems [J].
Dureau, Joseph ;
Kalogeropoulos, Konstantinos ;
Baguelin, Marc .
BIOSTATISTICS, 2013, 14 (03) :541-555
[10]   Robust extended Kalman filtering [J].
Einicke, GA ;
White, LB .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1999, 47 (09) :2596-2599
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